IEEE Transactions on CyberneticsIEEE 控制论汇刊
IEEE Transactions on Cybernetics(英文缩写 IEEE T CYBERNETICS),ISSN 2168-2267,eISSN 2168-2275,中文译名:IEEE 控制论汇刊 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-11-01 至 2026-10-31，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 19.118 | Q1 |
| 2022 | 11.800 | Q1 |
| 2023 | 9.400 | Q1 |
| 2024 | 10.500 | Q1 |
| 2025 | 11.300 | Q1 |
IEEE Transactions on Cybernetics 最新收录文献
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1. Multilevel Fault Information Fusion Based on Deep Block-Wise Semi-Nonnegative Matrix Factorization for Industrial Fault Detection and Diagnosis.
1. 基于深度块状半非负矩阵分解的多级故障信息融合用于工业故障检测与诊断PMID:日期:2026-09-25This article proposes a deep block-wise semi-nonnegative matrix factorization (DBSNMF) model for multilevel fault information fusion in industrial fault detection and diagnosis (FDD). DBSNMF first partitions variables into subblocks through semi-nonnegative matrix factorization (Semi-NMF) and Fast Unfolding to extract localized fault-related features. In each variable subblock, a deep Semi-NMF (DSNMF) architecture is built by stacking multilayer Semi-NMF models over concatenated feature and residual subspaces, thereby jointly exploiting information from both spaces. Then, we design a multilevel information fusion approach based on Bayesian inference and a fault-highlighted weighting strategy to fuse layer-, block-, and subspace-level information into a comprehensive fault detection statistic and a unified diagnosis index. Experiments on the continuous stirred tank reactor (CSTR) and Tennessee Eastman (TE) processes show that DBSNMF achieves the highest fault detection rates (FDRs) and the best abnormal-variable diagnosis performance.
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2. Multiobjective Evolutionary Diffusion Transformer Acceleration.
2. 多目标进化扩散Transformer加速PMID:日期:2026-09-25Diffusion Transformers (DiTs) have demonstrated outstanding performance in the field of image synthesis. However, the prohibitive computational overhead of model inference has been a major hurdle for deployment in low-latency scenarios. Based on the repetitive nature of the denoising process, we propose a multiobjective evolutionary algorithm for DiT acceleration, dubbed MEDA. Particularly, the conflict between the computational cost of the sampling process and the degradation in generation quality is explicitly modeled as a two-objective optimization problem, and similar intermediate features in adjacent sampling steps are stored and reused according to evolutionary-optimized schedules. Besides, a dynamic condition evaluation strategy is designed and incorporated into the evolutionary algorithm to maintain the generation performance of accelerated DiT models across different condition inputs. In addition, a latent objective normalization strategy is introduced to provide a bounded objective space for the evolution process. Experiments across various DiT architectures demonstrate that the proposed MEDA can significantly accelerate the sampling process while maintaining or even further enhancing generation quality relative to the original baseline models.
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3. Dynamic Event-Triggered Adaptive Fault-Tolerant Bipartite Formation-Containment Control for MASs via UIO.
3. 基于UIO的多智能体系统动态事件触发自适应容错二分编队包容控制PMID:日期:2026-09-24This article investigates the adaptive fault-tolerant time-varying bipartite formation-containment (TVBFC) problem of multiagent systems (MASs) subject to sensor and actuator faults. The MASs consist of multiple formation leaders (FLs), followers, and one autonomous tracking leader (TL). To begin with, for the followers and FLs to deal with the sensor faults, an output transformation is introduced, and thus an auxiliary output that decouples sensor faults is obtained. Subsequently, for coping with the nonlinear disturbance and actuator faults, a new control-augmented unknown input (CAUI) is constructed. Then, an unknown input observer (UIO), which is able to estimate the states, the CAUI, and the sensor faults simultaneously, is designed via an interval observer. Besides, a Luenberger observer (LO) is designed for estimating the state and sensor fault of the autonomous TL. An adaptive UIO-based dynamic event-triggered control (DETC) protocol is proposed for realizing TVBFC of MASs, and the Zeno behaviors of FLs and followers are also excluded. Finally, three simulation examples are given to demonstrate the effectiveness and advantages of the proposed methods.
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4. Distributed Cooperative Control Path Planning for UAV Formation Based on Ordered Trigger Communication.
PMID:日期:2026-09-23To address issues such as control instability and imbalance caused by communication constraints, this article proposes a distributed cooperative control method for multi-uncrewed aerial vehicle (UAV) formations based on communication-ordered triggering. First, to address the limited interactions caused by obstacle occlusion and other factors, a nonoverlapping conflict-staggered communication triggering mechanism is introduced. By incorporating intermittent neighboring communication principles, UAVs maximize information sharing within a limited communication window, effectively resolving issues such as channel conflicts and resource wastage caused by periodic broadcasting in existing communication methods. Next, a gradient-adaptive anti-saturation constraint model is developed to resolve the problems of large control parameter adjustments and slow convergence inherent in traditional anti-saturation methods. In addition, an optimized distributed cooperative orientation control strategy is constructed by integrating position and attitude information, preventing formation instability caused by the reliance on a single observation under interaction constraints. This approach significantly reduces the communication burden while enhancing the stability and adaptability of multi-UAV control. The experimental results show that the proposed communication-ordered triggering multi-UAV formation distributed cooperative control strategy exhibits global asymptotic stability. Compared to the adaptive optimization control method, this approach can maintain effective target tracking and formation stability under communication constraints. Moreover, it provides the UAV swarm with the ability for autonomous target tracking and formation reconfiguration. The communication frequency is significantly reduced compared to the cooperative event-triggered mechanism (CETM) and periodic event-triggered mechanism (PETM) methods, thereby reducing communication bandwidth usage, which aligns with the practical flight requirements of multi-UAV systems.
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5. Output Feedback Cooperative-Antagonistic Average Consensus in High-Order Multiagent Systems Over Directed Graphs.
5. 有向图上高阶多智能体系统中的输出反馈合作-对抗平均一致性PMID:日期:2026-09-23Output average consensus of cooperative/ cooperative-antagonistic multiagent systems (MASs) is of great significance in the engineering field, but is rarely addressed for general linear high-order networks interacting over directed graphs. This problem is herein tackled and solved via a novel hierarchical distributed output feedback control architecture, where the higher level embeds a distributed reference generator while the lower one is characterized by a proportional-integral-derivative (PID)-like control structure. The former is responsible for gathering the networked information to compute the output average agreement, which is exploited by the latter for the achievement of the coordination task. Combining the regulator equations and the static output feedback (SOF) procedure, properly extended in the distributed context, a well-defined closed-loop network is derived, and the control gain tuning procedure is provided. The versatility of the proposed approach in also handling the heterogeneous case, arising when agents do not share the same dynamical model, is also proved. Theoretical results establish the stability conditions via the Lyapunov-based analysis and the input-to-state stability (ISS) theory, and prove the accomplishment of the output unsigned/signed average consensus objectives with transient and steady-state performance improvements. Illustrative examples further corroborate the validity of the theoretical results.
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6. Distributed Dynamic Event-Triggered Formation Tracking Control for Multiagent Systems via a Unified Update Mechanism.
6. 基于统一更新机制的多智能体系统分布式动态事件触发编队跟踪控制PMID:日期:2026-09-23This article investigates the distributed formation tracking control of nonlinear multiagent systems (MASs) in the presence of unknown dynamics and external disturbances, with a particular focus on communication efficiency. To simultaneously address accurate estimation and communication savings, a unified dynamic event-triggered framework integrating observer and controller updates is proposed. In particular, an event-triggered extended state observer (ETESO) enhanced by a bias radial basis function neural network (BRBFNN) is developed, capable of capturing system uncertainties and external disturbances. Subsequently, a distributed dynamic event-triggered control scheme is designed, enabling each agent to update its ETESO and controller simultaneously and adaptively through dynamic triggering thresholds. Rigorous theoretical analysis is provided to confirm the semiglobal uniform ultimate boundedness (SGUUB) of the closed-loop system. Compared with existing fixed-threshold or controller-only triggering schemes, simulation results confirm that the proposed unified update framework simplifies system architecture while significantly enhancing estimation accuracy, tracking performance, and communication efficiency.
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7. A Distributed Protocol to Virtualize All-to-All Connectivity in a Class of Multiagent Systems.
7. 一种用于在一类多智能体系统中虚拟化全连接通信的分布式协议PMID:日期:2026-09-23This article addresses the challenge of achieving virtual full connectivity in multiagent systems (MASs) through a novel distributed protocol. Leveraging finite-time convergent observers, the proposed method enables each agent to estimate the states of all nonneighboring agents, facilitating seamless coordination in scenarios where direct communication links are absent. Unlike conventional approaches that focus on leader-following or partial connectivity frameworks, this method ensures mutual state estimation among all agents, thus enabling scalable control. The proposed model incorporates physical constraints, such as bounded agent velocity, which ensures practical realizability and allows the appropriate tuning of observer parameters to guarantee convergence within a finite time. A smooth transition function is introduced to integrate estimated states into the interaction dynamics, ensuring stability and continuity during the transition to a virtually fully connected topology. The system's properties, including collision avoidance, are preserved throughout this transition by deriving a new potential function that accounts for both directly connected and virtually connected agents. The theoretical contributions are validated through a rigorous analysis of convergence properties and the preservation of key interaction rules. Numerical results demonstrate the applicability of the proposed approach.
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8. A Distributed Monitoring Framework for Large-Scale Industrial Processes Based on Differential Grouping and Hierarchical Cooperative Modeling.
8. 基于差分分组和分层协同建模的大规模工业过程分布式监控框架PMID:日期:2026-09-23Modern large-scale industrial processes, characterized by high dimensionality, strong nonlinearity, and complex spatiotemporal couplings, pose significant challenges to traditional monitoring methods. To address these challenges, this article proposes a novel distributed monitoring framework: differential grouping and hierarchical cooperative monitoring (DG-HCM). First, a differential grouping (DG) strategy leverages deep feature interactions to adaptively decompose the process into functionally cohesive sub-blocks. Second, a hierarchical cooperative monitoring system is constructed upon this decomposition: a multiscale convolutional autoencoder (MSCAE) captures intrablock dynamics; canonical correlation analysis (CCA) with a spatiotemporal context monitors interblock collaborations; and a global statistic integrating all CCA canonical scores assesses system-wide coordination. Finally, all local statistics are fused into a unified decision index via Bayesian inference, while a hierarchical contribution analysis enables precise traceability from faulty sub-blocks to root-cause variables. Extensive validations on a numerical simulation, the Tennessee Eastman (TE) process, and a real-world industrial wastewater treatment plant (WWTP) demonstrate that the proposed framework outperforms advanced benchmarks in both monitoring performance and diagnostic interpretability.
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9. Maximum Correntropy Kalman Filter-Based Robust Model-Free Adaptive Control for MIMO Nonlinear Systems Under Non-Gaussian Noise.
PMID:日期:2026-09-22In this article, a robust model-free adaptive control (MFAC) algorithm is proposed based on the maximum correntropy Kalman filter (MCKF) for a class of multiple-input-multiple-output discrete-time nonlinear systems subject to non-Gaussian noise. The proposed algorithm relies solely on input-output data without requiring any model information, effectively suppressing output fluctuations caused by non-Gaussian noise while ensuring satisfactory tracking performance. First, a modified linear data model is constructed using partial-form dynamic linearization (DL). Leveraging this model, a fixed-point iteration-based MCKF is designed to derive posterior output estimates characterized by reduced uncertainty and enhanced smoothness. Subsequently, a cost function involving the posterior tracking error and its difference term is formulated using the posterior output estimates, based on which a robust MFAC algorithm is derived. Furthermore, the mean-square exponential boundedness of the posterior output estimation error and the boundedness of the expectation of the tracking error norm are rigorously established. Finally, the effectiveness and robustness of the proposed algorithm are verified through a numerical simulation and a blast furnace (BF) ironmaking data-based case study.
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10. Predefined-Time Output Tracking Control of Nonlinear Systems With Uncertain Parameters Under Denial-of-Service Attacks.
10. 拒绝服务攻击下具有不确定参数的非线性系统预定时间输出跟踪控制PMID:日期:2026-09-22This article investigates the predefined-time output tracking control of nonlinear systems consisting of multiple output-coupled subsystems with uncertain parameters and subject to edge- and node-based denial-of-service (DoS) attacks. The main challenge lies in achieving predefined-time tracking in the presence of both unknown nonlinear dynamics and complex DoS attack patterns. To address this challenge, fuzzy neural networks (FNNs) are employed to approximate the unknown nonlinear dynamics, and an output-feedback controller is developed based on a generalized function. The proposed control scheme provides a unified treatment of edge- and node-based DoS attacks, under which the tracking error converges to zero within the specified time. In addition, sufficient conditions are established to guarantee the predefined-time output tracking of nonlinear systems with uncertain parameters under edge- and node-based DoS attacks. The continuity and boundedness of the control input are also rigorously guaranteed, which enhances the practical implementation of the proposed controller. Finally, numerical examples are conducted to validate the theoretical results and demonstrate the effectiveness of the proposed method under different attack and fault scenarios.